Printed from https://fiscalreceipts.com/program/0603040A/ — data as of July 28, 2026. Every figure is citation-backed; see the page online for per-number provenance.
Artificial Intelligence and Machine Learning Advanced Technologies
Budget Figures
FY2026 award data is a partial year — USASpending awards are reported on a rolling basis and the fiscal year does not close until September 30. why partial FY2026 data? →
- FY24
- $23.9M
- FY25
- $30.3M
- FY26
- $20.5M
● actuals (line) · ○ enacted · ◇ request — gaps are editions the program is absent from, never interpolated.
| Series | FY20 | FY21 | FY22 | FY23 | FY24 | FY25 | FY26 |
|---|---|---|---|---|---|---|---|
| Actuals | $0 | $0 | $876.0K | $6.16M | $23.9M | ||
| Enacted | – | $0 | $909.0K | $6.39M | $13.2M | $30.3M | |
| Request | – | – | $909.0K | $6.39M | $13.2M | $18.3M | $20.5M |
blank = series not published for this year; – = absent from that edition.
Asked vs spent: the PB2024 book requested $13.2M for FY2024; the PB2026 book reports $23.9M actually spent — $10.7M above the request.
Program Lineage
No predecessor/successor lineage was recorded for this program element — no FY-to-FY transfer into or out of this line was stated in the ingested J-books, and none was inferred from the program structure.
Description
Mission — AI-Enabled Command and Coordination Adv Tech
This Project matures and demonstrates solutions for Artificial Intelligence (AI)-enabled Command and Coordination (C2) that provide timely understanding and application of the commander's intent. This Project improves sensor-to-shooter and course of action development timelines by developing algorithms, software, and hardware to efficiently capture, transport, process, and convey complex battlefield data into user friendly, streamlined, interfaces. This Project also exploits advances in the application of game theory to explore hypothetical operational scenarios that inform mission planning. These technologies will optimize mission command and network capabilities to fully realize AI on the battlefield. These technologies will produce software, novel algorithms and models, and knowledge products that focus on enabling commanders and their staffs with the ability to conduct mission command to achieve C2 overmatch. Work in this Project complements Program Element (PE) 0602180A (Artificial Intelligence and Machine Learning Technologies) / Project DA6 (AI-Enabled Command and Coordination Apl Research). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — Army AI Integration Center Adv Research (CA)
Congressional Interest Item funding provided for Army AI Integration Center Advanced Research. The cited work is consistent with the Under Secretary of Defense for Research and Engineering priority focus areas and the Army modernization strategy.
Mission — Predictive Maintenance Advanced Technology
This Project matures and demonstrates artificial intelligence (AI) and machine learning (ML) tools and capabilities to predict and analyze maintenance status for emerging and legacy aviation and ground platforms. Will extract maintenance data from databases and sensors and make inferences of missing data via virtual simulations and improve and provide AI data capture and other AI tools for enterprise maintenance resource planning for military aviation and ground vehicles. Platforms of focus will be prioritized by cost and value to Army missions and include the UH60, AH64, CH47, Stryker, and Abrams. Each platform will be sequentially evaluated both at the component (i.e. engine health) and fleet level. This Project matures and demonstrates the use of predictive maintenance to increase fleet operational readiness through reduced downtime by preventing critical failure during missions to maximize availability to combatant commands. Results from this project will inform requirements and technical architectures for a predicative maintenance platform that will include data engineering, data pipelines, AI development eco-system, and application delivery. These technologies will produce a suite of applications hosted at both the enterprise and at the edge that provide AI-enabled support tools decision-making for maintainers and commanders. Work in this project complements Program Element (PE) 0602180A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project CN7 (Predictive Maintenance Applied Research). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — ATR Using Multiple Cooperative Sensors Adv Tech
This Project will mature and demonstrate Artificial Intelligence (AI) algorithms/models and supporting systems that leverage a team of air and ground sensors to autonomously navigate and collaborate through shared perception of the optical, thermal, and electromagnetic spectrums to find, identify, geo-locate, track, and help engage targets during reconnaissance missions. These technologies will produce a mix of fully integrated software, AI algorithms/models, and ground-based/aerial-based drones to execute reconnaissance and engagement tasks in battlefield conditions. Work in this project complements Program Element (PE) 0602180A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project CL7 (ATR Using Multiple Cooperative Sensors App Tech) The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army modernization strategy. Work in this Project supports the Army Science and Technology Lethality Portfolio and the Joint Artificial Intelligence Center (JAIC).
Mission — AI Enhanced Intel Operations Advanced Technologies
Artificial Intelligence (AI) Enabled Intelligence Fusion for Targeting will address a "multi-INT" fusion problem and mature and demonstrate how AI algorithms can fuse data from various military intelligence systems to support sensor to shooter automation for the strategic, operational, and tactical levels. This effort will mature and demonstrate AI capabilities for support of Long-Range Precision Fires, Mission Command, and Maneuver Commanders by exploiting Intelligence Community enterprise investments in sensing, data transport, and Machine Learning (ML) / AI frameworks. These technologies will produce software, novel algorithms and models, and knowledge products. Work in this project complements Program Element (PE) 0602180A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project CL2 (AI Enhanced Intel Operations Technologies). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — Artificial Intelligence and Machine Learning Advanced Technologies
This Program Element (PE) will mature and demonstrate advanced technologies using artificial intelligence (AI) and machine learning (ML) to improve target recognition/detection using multiple cooperative autonomous sensors, leader decision-making, and replication of tactical behaviors to enable autonomous capabilities for maneuver, predictive maintenance, talent management, Intel support for Operations, network and cybersecurity and medical support. The Army's Artificial Intelligence Integration Center (AI2C) will provide strategic guidance and coordination of these advanced research efforts in AI/ML across the Army Modernization enterprise. Research in this PE contributes to the Army Science and Technology (S&T) portfolio and is fully coordinated with efforts in PE 0601601A (Artificial Intelligence and Machine Learning Basic Research) and PE 0602180A (Artificial Intelligence and Machine Learning Technologies). The cited research is consistent with the Under Secretary of Defense for Research and Engineering S&T focus areas, the Army Modernization Strategy and the Chief Digital and Artificial Intelligence Office (CDAO).
Mission — AI Development Environment Advanced Technology
This Project funds the Army lacking a common platform to develop AI/ML. This results in siloed and duplicative work that is inefficient. Many current solutions have narrow application and are proprietary, requiring additional funding, time, and labor to make even minor modifications. The AI-enabled Army of the future will require low cost, rapid AI/ML solutions at the edge. This project will mature and demonstrate a set of platform(s), and infrastructure optimized for Army use and ready for rapid employment in enterprise, multi, and hybrid cloud environments to support modular software (cloud native) intended to continuously develop and integrate AI/ML models. It will mature and demonstrate hardware and software technologies, including cloud native applications and infrastructure for globally dispersed AI/ML development collaboration, artifact sharing, automated resource provisioning, and continuous ML Operations. The AI Development Environment will provide the AI-enabled Army of the future with low cost, rapid AI/ML solutions at the edge and accelerated algorithm development for faster delivery to the field.as well as less expensive AI/ML development by leveraging shared resources.These technologies will produce a software prototype deployed in a cloud environment to demonstrate ability to conduct distributed development of AI/ML solutions. Work in this project complements Program Element (PE) 0602180A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project DE8 (AI Development Environment Applied Research). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — AI Enabled Contested Logistics Spt Tools Adv Tech
This project provides AI-enabled contested logistics tools to warfighters for all platforms (legacy and future) at all echelons. This effort will improve data from systems of record and leverage additional data streams to provide a complete picture of logistics and sustainment operations in contested environments. This project will provide analysis of maintenance operations, asset visibility, and people personnel capacity to assess current and predict future unit readiness and reduce to logistics and sustainment decision making timelines in contested environments. These technologies will provide a suite of applications uniquely tailored to the end-user that demonstrates machine learning capabilities across the force with regards to contested logistics. Work in this Project complements Program Element (PE) 0603040A (Artificial Intelligence and Ma chine Learning Advanced Technologies) / Project CN6 (Predictive Maintenance Advanced Technology). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — Predictive Maintenance Advanced Technology
This Project matures and demonstrates artificial intelligence (AI) and machine learning (ML) tools and capabilities to predict and analyze maintenance status for emerging and legacy aviation and ground platforms. Will extract maintenance data from databases and sensors and make inferences of missing data via virtual simulations and improve and provide AI data capture and other AI tools for enterprise maintenance resource planning for military aviation and ground vehicles. Platforms of focus will be prioritized by cost and value to Army missions and include the UH60, AH64, CH47, Stryker, and Abrams. Each platform will be sequentially evaluated both at the component (i.e. engine health) and fleet level. This Project matures and demonstrates the use of predictive maintenance to increase fleet operational readiness through reduced downtime by preventing critical failure during missions to maximize availability to combatant commands. Results from this project will inform requirements and technical architectures for a predicative maintenance platform that will include data engineering, data pipelines, AI development eco-system, and application delivery. These technologies will produce a suite of applications hosted at both the enterprise and at the edge that provide AI-enabled support tools decision-making for maintainers and commanders. Work in this project complements Program Element (PE) 0602180A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project CN7 (Predictive Maintenance Applied Research). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — ATR Using Multiple Cooperative Sensors Adv Tech
This Project will mature and demonstrate Artificial Intelligence (AI) algorithms/models and supporting systems that leverage a team of air and ground sensors to autonomously navigate and collaborate through shared perception of the optical, thermal, and electromagnetic spectrums to find, identify, geo-locate, track, and help engage targets during reconnaissance missions. These technologies will produce a mix of fully integrated software, AI algorithms/models, and ground-based/aerial-based drones to execute reconnaissance and engagement tasks in battlefield conditions. Work in this project complements Program Element (PE) 0602180A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project CL7 (ATR Using Multiple Cooperative Sensors App Tech) The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army modernization strategy. Work in this Project supports the Army Science and Technology Lethality Portfolio and the Joint Artificial Intelligence Center (JAIC).
Mission — AI Enhanced Intel Operations Advanced Technologies
Artificial Intelligence (AI) Enabled Intelligence Fusion for Targeting will address a "multi-INT" fusion problem and mature and demonstrate how AI algorithms can fuse data from various military intelligence systems to support sensor to shooter automation for the strategic, operational, and tactical levels. This effort will mature and demonstrate AI capabilities for support of Long-Range Precision Fires, Mission Command, and Maneuver Commanders by exploiting Intelligence Community enterprise investments in sensing, data transport, and Machine Learning (ML) / AI frameworks. These technologies will produce software, novel algorithms and models, and knowledge products. Work in this project complements Program Element (PE) 0602180A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project CL2 (AI Enhanced Intel Operations Technologies). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — Artificial Intelligence and Machine Learning Advanced Technologies
This Program Element (PE) will mature and demonstrate advanced technologies using artificial intelligence (AI) and machine learning (ML) to improve target recognition/detection using multiple cooperative autonomous sensors, leader decision-making, and replication of tactical behaviors to enable autonomous capabilities for maneuver, predictive maintenance, talent management, Intel support for Operations, network and cybersecurity and medical support. The Army's Artificial Intelligence Integration Center (AI2C) will provide strategic guidance and coordination of these advanced research efforts in AI/ML across the Army Modernization enterprise. Research in this PE contributes to the Army Science and Technology (S&T) portfolio and is fully coordinated with efforts in PE 0601601A (Artificial Intelligence and Machine Learning Basic Research) and PE 0602180A (Artificial Intelligence and Machine Learning Technologies). The cited research is consistent with the Under Secretary of Defense for Research and Engineering S&T focus areas, the Army Modernization Strategy and the Chief Digital and Artificial Intelligence Office (CDAO).
Mission — AI Enabled Contested Logistics Spt Tools Adv Tech
This project provides AI-enabled contested logistics tools to warfighters for all platforms (legacy and future) at all echelons. This effort will improve data from systems of record and leverage additional data streams to provide a complete picture of logistics and sustainment operations in contested environments. This project will provide analysis of maintenance operations, asset visibility, and people personnel capacity to assess current and predict future unit readiness and reduce to logistics and sustainment decision making timelines in contested environments. These technologies will provide a suite of applications uniquely tailored to the end-user that demonstrates machine learning capabilities across the force with regards to contested logistics. Work in this Project complements Program Element (PE) 0603040A (Artificial Intelligence and Ma chine Learning Advanced Technologies) / Project CN6 (Predictive Maintenance Advanced Technology). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — AI Development Environment Advanced Technology
This Project funds the Army lacking a common platform to develop AI/ML. This results in siloed and duplicative work that is inefficient. Many current solutions have narrow application and are proprietary, requiring additional funding, time, and labor to make even minor modifications. The AI-enabled Army of the future will require low cost, rapid AI/ML solutions at the edge. This project will mature and demonstrate a set of platform(s), and infrastructure optimized for Army use and ready for rapid employment in enterprise, multi, and hybrid cloud environments to support modular software (cloud native) intended to continuously develop and integrate AI/ML models. It will mature and demonstrate hardware and software technologies, including cloud native applications and infrastructure for globally dispersed AI/ML development collaboration, artifact sharing, automated resource provisioning, and continuous ML Operations. The AI Development Environment will provide the AI-enabled Army of the future with low cost, rapid AI/ML solutions at the edge and accelerated algorithm development for faster delivery to the field.as well as less expensive AI/ML development by leveraging shared resources.These technologies will produce a software prototype deployed in a cloud environment to demonstrate ability to conduct distributed development of AI/ML solutions. Work in this project complements Program Element (PE) 0602180A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project DE8 (AI Development Environment Applied Research). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — Army AI Integration Center Adv Research (CA)
Congressional Interest Item funding provided for Army AI Integration Center Advanced Research. The cited work is consistent with the Under Secretary of Defense for Research and Engineering priority focus areas and the Army modernization strategy.
Mission — AI-Enabled Command and Coordination Adv Tech
This Project matures and demonstrates solutions for Artificial Intelligence (AI)-enabled Command and Coordination (C2) that provide timely understanding and application of the commander's intent. This Project improves sensor-to-shooter and course of action development timelines by developing algorithms, software, and hardware to efficiently capture, transport, process, and convey complex battlefield data into user friendly, streamlined, interfaces. This Project also exploits advances in the application of game theory to explore hypothetical operational scenarios that inform mission planning. These technologies will optimize mission command and network capabilities to fully realize AI on the battlefield. These technologies will produce software, novel algorithms and models, and knowledge products that focus on enabling commanders and their staffs with the ability to conduct mission command to achieve C2 overmatch. Work in this Project complements Program Element (PE) 0602180A (Artificial Intelligence and Machine Learning Technologies) / Project DA6 (AI-Enabled Command and Coordination Apl Research). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Justification
Accomplishments & Planned Programs (24)
AI Foundations for Command and Coordination
Matures and optimizes novel foundational models in computer vision, natural language processing/understanding, and temporal/event series analysis that analyze, understand, and optimize AI-operations across Army Battle Command Systems and data fabrics. Establishes access to fused multitudinous data sources in support of AI-based analytics capabilities.
AI-Enabled Common Operating Picture and Battle Tracking
This effort will develop and mature AI-enabled tools that allow commanders and staff to prepare for, execute, and assess Army operations to enable decision dominance. Will mature and demonstrate human-machine interfaces that take input of commanders' intent and plans and provides computer-based battle tracking to identify risk to mission and force and AI-optimized direction to Army forces and unified action partners.
PMx Autonomous Resupply
This effort will develop, mature, and demonstrate AI models and algorithms for an autonomous aviation platform to transport supply stocks to support operations. Emphasis will be on ensuring the airworthiness of an autonomous aviation platform that can move from a rear resupply point forward to a designated location while avoiding basic obstacles and accounting for normal weather conditions. Resupply will occur using human intervention after the autonomous aircraft safely stops in the designated end location.
PMx Platform Data Management and Integrated Environment Refinement
This effort will mature and optimize a predictive maintenance (PMx) cloud-based environment, mature and validate data collection/aggregation techniques, and demonstrate and validate a data architecture and the data pipelines to a cloud-based environment.
Foundation for AI Intel Support to Operations
Develop and mature an AI infrastructure/pipeline for training, integrating, and sustaining AI across multiple AI domains to inform requirements for enterprise production systems and edge systems for the Army Military Intelligence and Operations (Intel/Ops) community.
AI Enabled Intelligence Fusion for Targeting
AI Enabled Intelligence Fusion for Targeting will mature and demonstrate how AI algorithms can fuse data from various military intelligence systems (multi-INT) to support sensor to shooter automation for the strategic, operational, and tactical levels. This effort will design and develop AI capabilities for support of Long Range Precision Fires, Mission Command, and Maneuver Commanders by leveraging Intelligence Community enterprise investments in sensing, data transport, and Machine Learning / AI frameworks.
Federated Predictive Logistics Adv Tech Dev
This effort provides predictive logistics analytics by leveraging the collection and input of structured, quality data from the warfighter and networked sensors; validated and verified algorithms; and by leveraging artificially intelligent modeling machine learning models for use by maintainers, warfighters, and commanders to identify and quantify risk, effectively allocate and prioritize resources, and assess future courses of action in support of logistics and sustainment operations in contested environments.
Contested Logistics Decision Support Tools
This effort will mature a light weight, containerized environment technologies that allows access to logistics and sustainment support tools in both an enterprise and disconnected, denied, intermittent and/or with limited bandwidth (DDIL) environment. This effort will demonstrate the utility of leveraging machine learning models for use by warfighters to identify and quantify risk, effectively allocate and prioritize resources, and assess future courses of action in support of logistics and sustainment operations in a contested environment. This effort will leverage machine learning models and analyze data incorporating unit operations, personnel and training to assess and predict unit readiness and future unit effectiveness conducting logistics and sustainment operations in contested environments.
Artificial Intelligence Development Environment Advanced Technology Development
Will mature and optimize a cloud native AI model development architecture, mature and validate data integration techniques, and demonstrate and validate an AI model operationalization architecture to cloud or edge endpoints.
Soldier Assistant Language Technologies
This effort will investigate and mature application of cutting-edge language technologies onto warfighter systems in order to increase network effectiveness and resilience, reduce personnel requirements, and increase Solder situational awareness. Exploitation of semantic understanding, machine translation, natural language processing, automated speech recognition and other emerging language-based technologies and techniques to enable decisions at machine speed, expanding the scope of useful data to include natural language.
AI Enhanced Planning for Optimal Operations
Designs and develops AI-enabled systems that link people, processes, networks, and command posts in support of command and control. Develops and trains models that analyze, understand, and optimize AI-operations across Army Battle Command Systems and data fabrics. Establishes access to fused multitudinous data sources in support of AI-based analytics capabilities.
Collaborative Target Detection and Tracking
This effort will mature and demonstrate an AI-enabled scalable team of autonomous air and ground vehicles that will cooperatively conduct a zone recon to identify, geolocate, and track threats using on-board electronic intelligence (ELINT) and electro optical-infrared (EO-IR) sensors.
Soldier Assistant Language Technologies
This effort will investigate and mature application of cutting-edge language technologies onto warfighter systems in order to increase network effectiveness and resilience, reduce personnel requirements, and increase Solder situational awareness. Exploitation of semantic understanding, machine translation, natural language processing, automated speech recognition and other emerging language-based technologies and techniques to enable decisions at machine speed, expanding the scope of useful data to include natural language.
Collaborative Target Detection and Tracking
This effort will mature and demonstrate an AI-enabled scalable team of autonomous air and ground vehicles that will cooperatively conduct a zone recon to identify, geolocate, and track threats using on-board electronic intelligence (ELINT) and electro optical-infrared (EO-IR) sensors.
Foundation for AI Intel Support to Operations
Develop and mature an AI infrastructure/pipeline for training, integrating, and sustaining AI across multiple AI domains to inform requirements for enterprise production systems and edge systems for the Army Military Intelligence and Operations (Intel/Ops) community.
AI Enabled Intelligence Fusion for Targeting
AI Enabled Intelligence Fusion for Targeting will mature and demonstrate how AI algorithms can fuse data from various military intelligence systems (multi-INT) to support sensor to shooter automation for the strategic, operational, and tactical levels. This effort will design and develop AI capabilities for support of Long Range Precision Fires, Mission Command, and Maneuver Commanders by leveraging Intelligence Community enterprise investments in sensing, data transport, and Machine Learning / AI frameworks.
Federated Predictive Logistics Adv Tech Dev
This effort provides predictive logistics analytics by leveraging the collection and input of structured, quality data from the warfighter and networked sensors; validated and verified algorithms; and by leveraging artificially intelligent modeling machine learning models for use by maintainers, warfighters, and commanders to identify and quantify risk, effectively allocate and prioritize resources, and assess future courses of action in support of logistics and sustainment operations in contested environments.
PMx Platform Data Management and Integrated Environment Refinement
This effort will mature and optimize a predictive maintenance (PMx) cloud-based environment, mature and validate data collection/aggregation techniques, and demonstrate and validate a data architecture and the data pipelines to a cloud-based environment.
Artificial Intelligence Development Environment Advanced Technology Development
Will mature and optimize a cloud native AI model development architecture, mature and validate data integration techniques, and demonstrate and validate an AI model operationalization architecture to cloud or edge endpoints.
AI Foundations for Command and Coordination
Matures and optimizes novel foundational models in computer vision, natural language processing/understanding, and temporal/event series analysis that analyze, understand, and optimize AI-operations across Army Battle Command Systems and data fabrics. Establishes access to fused multitudinous data sources in support of AI-based analytics capabilities.
AI Enhanced Planning for Optimal Operations
Designs and develops AI-enabled systems that link people, processes, networks, and command posts in support of command and control. Develops and trains models that analyze, understand, and optimize AI-operations across Army Battle Command Systems and data fabrics. Establishes access to fused multitudinous data sources in support of AI-based analytics capabilities.
PMx Autonomous Resupply
This effort will develop, mature, and demonstrate AI models and algorithms for an autonomous aviation platform to transport supply stocks to support operations. Emphasis will be on ensuring the airworthiness of an autonomous aviation platform that can move from a rear resupply point forward to a designated location while avoiding basic obstacles and accounting for normal weather conditions. Resupply will occur using human intervention after the autonomous aircraft safely stops in the designated end location.
AI-Enabled Common Operating Picture and Battle Tracking
This effort will develop and mature AI-enabled tools that allow commanders and staff to prepare for, execute, and assess Army operations to enable decision dominance. Will mature and demonstrate human-machine interfaces that take input of commanders' intent and plans and provides computer-based battle tracking to identify risk to mission and force and AI-optimized direction to Army forces and unified action partners.
Contested Logistics Decision Support Tools
This effort will mature a light weight, containerized environment technologies that allows access to logistics and sustainment support tools in both an enterprise and disconnected, denied, intermittent and/or with limited bandwidth (DDIL) environment. This effort will demonstrate the utility of leveraging machine learning models for use by warfighters to identify and quantify risk, effectively allocate and prioritize resources, and assess future courses of action in support of logistics and sustainment operations in a contested environment. This effort will leverage machine learning models and analyze data incorporating unit operations, personnel and training to assess and predict unit readiness and future unit effectiveness conducting logistics and sustainment operations in contested environments.
Budget Line Items(workbook-cited)
Exhibit R-1
| Account | Org | Type | Amount |
|---|---|---|---|
| Research, Development, Test and Evaluation, Army | A | FY24 Actuals | $23.9M |
| Research, Development, Test and Evaluation, Army | A | FY25 Enacted | $30.3M |
| Research, Development, Test and Evaluation, Army | A | FY25 Total | $30.3M |
| Research, Development, Test and Evaluation, Army | A | FY26 Disc. Request | $20.5M |
| Research, Development, Test and Evaluation, Army | A | FY26 Total | $20.5M |
Budget Details(R-2/P-40 facts)
| Project | FY24 Actuals | FY25 Total | FY26 Base | FY26 Request |
|---|---|---|---|---|
| DN3: AI Enabled Contested Logistics Spt Tools Adv Tech | — | — | $748.0K | $748.0K |
| CL1: AI Enhanced Intel Operations Advanced Technologies | $1.31M | $2.26M | $1.86M | $1.86M |
| DE9: AI Development Environment Advanced Technology | $1.06M | $1.97M | $2.78M | $2.78M |
| DA7: AI-Enabled Command and Coordination Adv Tech | $1.34M | $1.16M | $3.25M | $3.25M |
| CN6: Predictive Maintenance Advanced Technology | $3.97M | $4.14M | $5.33M | $5.33M |
| CL6: ATR Using Multiple Cooperative Sensors Adv Tech | $4.73M | $8.74M | $6.53M | $6.53M |
| Program Element | $23.9M | $30.3M | $20.5M | $20.5M |
| CT8: Army AI Integration Center Adv Research (CA) | $11.5M | $12.0M | — | — |
No follow-the-dollar view — this program's awards haven't been crosswalked at high confidence (flows cover 17 of 1741 programs). why coverage is partial? →
Awards
No awards are linked to this program element at high confidence — the budget→award crosswalk only asserts links it can defend, and this line has none yet.
Lobbying Mentions
No Senate LDA lobbying filing in the tracked data mentions this program element by code or alias.
No research dossier for this program — dossiers cover 50 of 1741 programs, the largest fully J-book-detailed lines by FY2026 requested dollars. why no dossier here? →